The decision-making process is one of the complicated processes involved in warehouse operation for efficiently fulfilling various specific customer orders. This is especially true if the orders require cross-border delivery activities, such as palletization of the delivery goods according to regulation requirements. Case-based reasoning is an intelligent method for complex problem solving that uses past cases to find a solution to new problems. To achieve an appropriate solution, retrieving useful prior cases effectively for the problem is essential. However, current case retrieval methods are mainly based on a fixed set of attributes for different type of orders in which specific order features for case groups are neglected. In this paper a hybrid approach called the case-genetic algorithm-based decision support model (C-GADS), is proposed in classifying new customer orders into case groups with the highest similarity value, allowing for effectively selecting the most similar cases among the group. The proposed model also suggests the types of features considered in each case group. It helps enhance the effectiveness of formulating warehouse order operations based on grouping similar cases. To validate the feasibility of the proposed model, a case study is conducted and the results show that planning effectiveness is enhanced.
- Case retrieval
- Case-based reasoning
- Genetic algorithm-based clustering
- Warehouse operation planning
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications